Find Your Dream Python Job in India

Job Title: Back-end Python Developer

Experience: 4-5 Years

Location: Onsite - Koramangala / Adugodi, Bengaluru

Employment Type: Contract (1 Year)

Budget: Up to 1.2 Lakh per Month

Shift: General IST Shift

Role Overview

We are looking for a skilled Back-end Python Developer with strong experience in building scalable, high-performance backend systems. The ideal candidate should have hands-on expertise in FastAPI, microservices architecture, and big data processing pipelines, along with strong database optimization and cloud exposure.

Key Responsibilities

• Design, develop, and maintain scalable backend services using Python (FastAPI framework)
• Build and manage microservices-based architectures
• Develop and optimize high-performance data processing pipelines
• Work with asynchronous programming and concurrency models
• Implement caching mechanisms using Redis
• Optimize database queries and ensure system performance and scalability
• Integrate messaging systems such as Kafka
• Collaborate with DevOps and cloud teams for deployment and monitoring
• Maintain clean, reusable, and version-controlled code using Git

Required Skills & Expertise

• Strong experience in Python backend development
• Hands-on expertise with FastAPI
• Experience in Microservices Architecture
• Big Data & Pipeline Processing using:
• Kafka
• Apache Spark / Airflow
• Python Async Pipelines
• Redis for caching and performance optimization
• Strong understanding of Concurrency & Async Programming
• Database optimization and performance tuning
• Version control using Git
• Exposure to Cloud platforms (AWS/Azure/GCP preferred)

Preferred Skills

• Experience handling high-throughput systems
• Knowledge of distributed systems and scalable architecture
• Strong debugging and performance tuning skills

Engagement Details

• Contract Duration: 12 Months
• Work Mode: Onsite
• Immediate or short notice candidates preferred

Role Description

This is a full-time on-site role for a Python Full Stack Developer located in Pune. You will be responsible for end-to-end development of scalable, AI-driven web applications. Day-to-day tasks involve architecting asynchronous backend services using Python and FastAPI, building dynamic user interfaces with ReactJS, and managing cloud infrastructure on AWS. You will collaborate with data scientists and product teams to integrate AI models into enterprise solutions while ensuring high performance and reliability.

Key Responsibilities

1. Design and develop high-performance asynchronous APIs using Python and FastAPI.

2. Build responsive, interactive frontends using ReactJS, HTML, CSS, and Tailwind CSS.

3. Implement distributed task queues and caching mechanisms using Celery and Redis.

4. Architect and optimize databases, managing both structured (PostgreSQL) and unstructured (MongoDB) data.

5. Deploy and manage infrastructure on AWS (EC2, Lambda, S3) and maintain CI/CD pipelines for automated deployment.

6. Integrate AI/ML models into production workflows and optimize system performance for scalability.

7. Ensure application security, data privacy, and code quality through best practices and regular testing.

Required Skills & Qualifications

1. 4-7 years of experience in full-stack development with a strong focus on Python.

2. Proficiency in FastAPI and deep understanding of asynchronous programming (asyncio).

3. Solid experience with ReactJS, HTML, CSS, JavaScript, and Tailwind CSS.

4. Hands-on experience with Celery and Redis for background task processing.

5. Working knowledge of AWS services and containerization tools like Docker.

6. Proficiency in database management using PostgreSQL and MongoDB.

7. Experience setting up CI/CD pipelines (Jenkins, GitHub Actions, etc.) and version control (Git).

8. Strong understanding of RESTful API design, microservices, and security best practices.

Job Description

Exp: 5-12years

Location: Pune

Basic Qualifications:

Solid understanding of Microsoft ETL data ingestion framework specifically SQL Server, SSIS DevOps.

Solid understanding of SQL server development and management tools.

Solid understanding of file management, data storage management and operations on premise and

cloud.

Solid understanding of data warehousing and archiving tools.

Solid understanding of Apache Spark operations and monitoring.

Solid understanding of high-volume data processing frameworks on premise and Cloud.

Familiarity with data pipeline management frameworks on cloud (AWS, Azure, Google)

Familiarity with Python, R scripting and IDEs like Jupitar notebook, RStudio.

Preferred Qualifications: Familiarity with open-source data wrangling tools and technologies.

Familiarity with data access control and management tools.

Familiarity with Apache Hadoop, Hive, Impala etc.

Familiarity with NoSQL databases like MongoDB, Graph databases like Neo4j, Amazon Neptune etc.

Expertise in data pipeline management on any 1 Cloud (Amazon AWS, Microsoft Azure, Google Cloud).

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Responsibilities

• AI/ML & LLM Expertise:
• Design, fine-tune, and deploy small and open-source large language models (LLMs) such as Llama, Mistral, OpenAI GPT, etc.
• Hands-on leadership in prompt engineering, few-shot prompting, and building advanced NLP/NLU workflows.
• Guide adoption of modern AI/ML frameworks (Hugging Face Transformers, LangChain, LangGraph, etc.) and architect reusable pipelines in Python.
• Python & API Development:
• Drive critical systems architecture in Python, using best practices in API and microservices design (FastAPI, Flask, Django, etc.).
• Cloud Deployment (AWS/Azure/GCP):
• Architect, deploy, and scale robust, production-grade ML/AI solutions on cloud (AWS strongly preferred), leveraging cloud-native tools (Lambda, S3, ECS/ECR/Fargate, etc.), serverless, and IaC (CloudFormation/Terraform).
• Champion DevOps best practices, automation, containerization (Docker/K8s), CI/CD, and operational monitoring.
• Technical Leadership:
• Mentor engineers, lead by example, drive system architecture reviews and code standards, and ensure high-quality technical delivery across teams.
• Act as the technical point of contact for escalation, incident resolution, and production troubleshooting.

Requirements

• Experience:
• 8+ years in software development, including 3+ in senior or lead roles delivering ML/AI solutions in a cloud environment.
• LLM & Prompt Engineering:
• Strong real-world experience in LLM prompt engineering, few-shot prompting, and fine-tuning (using frameworks like Hugging Face, LangChain, LangGraph, etc.).
• Python Expertise:
• Mastery of Python for API/microservice development, object-oriented patterns, code optimization, automated testing, and packaging.
• Cloud (AWS Preferred):
• Hands-on deployment and scaling of AI/ML services on AWS, Azure, or GCP; proficient in containers, serverless, and infrastructure as code.
• Technical Leadership:
• Proven experience mentoring software engineers, shaping system design, and driving cross-team initiatives.
• Communication:
• Exceptional ability to explain complex technical subjects and influence technical direction with diverse audiences.

Nice to Have

• Databricks:
• Experience building, deploying, or orchestrating ML/AI or data pipelines on Databricks (Data Engineering, MLflow, collaborative workflows, jobs).
• (Note: Knowledge of Databricks is highly valued but not required; candidates without PySpark but with Databricks experience are welcome.)
• PySpark:
• Experience using PySpark for big data ETL/processing, but not a must-have.
• Data Engineering:
• Familiarity with Spark, Airflow, advanced data analytics stacks, and modern data lakes (e.g., Delta Lake).
• ML Productionization & MLOps:
• Experience with ML lifecycle tools, CI/CD pipelines, monitoring, and model governance.
• Visualization:
• Python-based dashboarding/analytics (Streamlit, Dash, Plotly).
• Security & Compliance:
• Secure cloud design, IAM, encryption, and compliance frameworks.
• Published Work / Open Source:
• Contributions to AI/ML communities, conference presentations, or technical publications.

Role : Python Django Lead

Experience Required - 10+ Years

Location - Indore/ Pune/ Bangalore/ Chennai (Hybrid)

Job Overview: We are seeking an experienced Django Backend Lead Developer to join our team. The ideal candidate will have a strong background in backend development, cloud technologies, and big data processing. This role involves leading technical projects, mentoring junior developers, and ensuring the delivery of high-quality solutions.

Responsibilities:

• Lead the development of backend micro services using Django.
• Design and implement scalable and secure APIs.
• Integrate Azure Cloud services for application deployment and management.
• Utilize Azure Databricks for big data processing and analytics.
• Implement data processing pipelines using PySpark.
• Collaborate with front-end developers, product managers, and other stakeholders to deliver comprehensive solutions.
• Conduct code reviews and ensure adherence to best practices.
• Mentor and guide junior developers.
• Optimize database performance and manage data storage solutions.
• Ensure high performance and security standards for applications.
• Participate in architecture design and technical decision-making.

Qualifications:

• Bachelor's degree in Computer Science, Information Technology, or a related field.
• 8+ ye

Project description

We need a Senior Python and Pytest Tester to work for a leading investment bank client

Responsibilities

Interact with product management, project management and development teams to develop a strong understanding of the project and testing objectives

Should co-ordinate between onshore & offshore teams

Perform SQL queries to analyze and manipulate data.

Participate in troubleshooting and triaging of issues with different teams to drive towards root cause identification and resolution

Design and create test conditions, test data and test scripts to address business and technical use cases

Use existing tools and techniques to execute test cases and build/script new tools for performing testing/validation function

Skills

Must have

5+ years of experience in data driven testing

Mandatory experience in Banking or Capital Market Projects

Good experience on PyTest and Test Automation

Good experience in Selenium

Strong understanding of web application testing and associated technologies.

Proficiency in creating test plans and test cases per requirements and design

Familiarity with continuous integration and continuous deployment (CI/CD) processes.

Nice to have

Knowledge of PySpark

Understanding of Financial Markets Asset Classes (FX, FI, Equities, Rates, Commodities &

Project description

We need a Senior Python and Pytest Tester to work for a leading investment bank client

Responsibilities

Interact with product management, project management and development teams to develop a strong understanding of the project and testing objectives

Should co-ordinate between onshore & offshore teams

Perform SQL queries to analyze and manipulate data.

Participate in troubleshooting and triaging of issues with different teams to drive towards root cause identification and resolution

Design and create test conditions, test data and test scripts to address business and technical use cases

Use existing tools and techniques to execute test cases and build/script new tools for performing testing/validation function

Skills

Must have

5+ years of experience in data driven testing

Mandatory experience in Banking or Capital Market Projects

Good experience on PyTest and Test Automation

Good experience in Selenium

Strong understanding of web application testing and associated technologies.

Proficiency in creating test plans and test cases per requirements and design

Familiarity with continuous integration and continuous deployment (CI/CD) processes.

Nice to have

Knowledge of PySpark

Understanding of Financial Markets Asset Classes (FX, FI, Equities, Rates, Commodities &

Experience: 7-12years

Location: Hyderabad, Pune, Coimbatore, Bangalore

Work Mode: Work from Office

We’re looking for a hands-on Python Lead Engineer with a strong focus on building user interfaces and frontend experiences using Python . This role is ideal for someone who enjoys creating interactive dashboards, internal tools, and user-facing UIs without relying heavily on JavaScript frameworks.

Key Responsibilities

• Lead the design and development of Python-based user interfaces
• Build interactive dashboards, tools, and applications for internal and external users
• Own UI architecture , state management, and layout design
• Translate product and business needs into usable, responsive interfaces
• Review code, mentor developers, and establish frontend/UI best practices in Python
• Collaborate closely with backend, data, and ML teams
• Drive technical decisions around Python UI frameworks and tooling

Experience building UIs using:

• Django templates / Jinja2
• Flask with HTML/CSS templating

UI & Frontend Concepts

• Hands-on experience with:
• UI components, widgets, and forms
• User interaction and input handling
• State management and session state
• Layouts and responsive design

Project Description:

Are you a Developer? Do you enjoy writing all sorts of source code (from simple scripts to fully fledged applications and frameworks)? Do you like scripting and process automation? Are you looking to specialize in IT Security?

Our role is a Developer with core skills including: Python, SQL (Oracle migrating to Postgres), Unix (RHEL), and releases using MS Azure and Ansible.

We're looking for a Software Engineer/Developer to:

• Provide technical expertise and recommendations in assessing new software projects and initiatives to support and enhance our existing applications
• Produce secure and clean code that is stable, operational, consistent, and well-performing
• Conduct code reviews and test software as needed, along with participating in application architecture and design, and other phases of SDLC
• See that proper operational controls and procedures are implemented in production
• Reduce as much as possible the overhead and manual activities by enhancements and automation

Your team

• Identity and Access Management ('IAM') is a global security organization within Technology Services. As an expert in IAM security and internet technologies, you'll be part of our team working closely with the internal project team. We provide privileged access invocation solutions. Our team is global, diverse, and collaborative as we wor

Project Description:

Are you a Developer? Do you enjoy writing all sorts of source code (from simple scripts to fully fledged applications and frameworks)? Do you like scripting and process automation? Are you looking to specialize in IT Security?

Our role is a Developer with core skills including: Python, SQL (Oracle migrating to Postgres), Unix (RHEL), and releases using MS Azure and Ansible.

We're looking for a Software Engineer/Developer to:

• Provide technical expertise and recommendations in assessing new software projects and initiatives to support and enhance our existing applications
• Produce secure and clean code that is stable, operational, consistent, and well-performing
• Conduct code reviews and test software as needed, along with participating in application architecture and design, and other phases of SDLC
• See that proper operational controls and procedures are implemented in production
• Reduce as much as possible the overhead and manual activities by enhancements and automation

Your team

• Identity and Access Management ('IAM') is a global security organization within Technology Services. As an expert in IAM security and internet technologies, you'll be part of our team working closely with the internal project team. We provide privileged access invocation solutions. Our team is global, diverse, and collaborative as we wor

Backend Engineer – SDE (Python/Golang)

Reporting to: Team Lead / Engineering Manager

Required Experience: 1-5 years

Location: Bangalore

Salary Range: Commensurate to experience and skills

Core Responsibilities

• Design, develop, and maintain backend services and APIs using Python or Golang.
• Write high-quality, testable, and maintainable code with a focus on performance and scalability.
• Implement automated tests and contribute to CI/CD pipelines.
• Collaborate with product, QA, and DevOps teams for end-to-end feature delivery.
• Troubleshoot production issues and provide timely resolutions.
• Participate in design and architecture discussions to improve system efficiency.
• Contribute to improving development processes, coding standards, and best practices.

Experience and Expertise

• 1–5 years of experience in backend development with Python or Golang.
• Solid understanding of RESTful APIs, microservices, and distributed systems.
• Strong knowledge of data structures, algorithms, and OOPS principles.
• Hands-on experience with relational and/or NoSQL databases.
• Familiarity with Linux development, Docker, and basic cloud concepts (AWS/GCP/Azure).
• Proficiency with Git and version control workflows.
• Familiarity with A

Python Full Stack Developer (PHP)

Bangalore: Onsite

Job Type: Permanent

Exp: 5+ Min

Responsibilities: Design, develop, and maintain data pipelines using Python (e.G., Pandas, NumPy, scikit-learn) for processing and analyzing large datasets

• Manage data pipelines in a continuous integration environment (e.G., Jenkins, CircleCI) to ensure automated testing, deployment, and monitoring of data processing jobs
• Collaborate with development teams to deploy web applications developed with PHP frameworks (Yii and Laravel) and other technologies using CI/CD tools (Jenkins, CircleCI)
• Ensure software quality by writing unit/functional/acceptance tests using Codeception, Pytest, and other testing frameworks
• Manage dependencies and packages using Composer (PHP) and Pip (Python)
• Develop and maintain automation scripts to streamline tasks, such as data processing, deployment, and testing
• Troubleshoot issues with data pipelines, applications, and CI/CD workflows
• Stay up-to-date with industry trends and best practices in DevOps, data engineering, and software development
• Requirements : Bachelor's degree in Computer Science, Data Science, or a related field
• 3+ years of experience in DevOps and data engineering with Python
• Experience with managing data pipelines in a continuous integration environment (e.G., Jen

Job Title: Python Automation Test Engineer

Location: Chennai (Sipcot IT Park, Siruseri )

Must Have:

Automation, Selenium, Python and Pytest

We are seeking a QA Automation Engineer (SDET) to design, develop, and maintain high-quality automated testing solutions across our UI, API, and mobile platforms. The ideal candidate is passionate about quality, experienced in Python-based automation frameworks, and comfortable working in a fast-paced, collaborative environment.

🔧 Tools & Technologies

- UI Automation: Python, Selenium- Pytest, Unittest

- API Testing: Postman/Newman, Python requests

- Mobile Automation: Appium

- Performance Testing: JMeter, Locust

- Version Control: GitHub

- CI/CD: GitHub Actions, CircleCI

📌 Key Responsibilities

- Build, enhance, and maintain automated test suites for UI, API, and mobile applications using the existing automation framework.

- Improve framework stability, performance, reporting, parallel execution, and overall maintainability.

- Execute automated test suites in CI/CD pipelines and debug, triage, and document failures with clear, actionable defect reports.

- Collaborate closely with QA, Engineering, and Product teams to understand requirements, define test strategies, and ensure robust coverage.

- Develop reusable automation components including page objects, utilities, and

Job Title: Python Automation Test Engineer

Location: Chennai (Sipcot IT Park, Siruseri )

Must Have:

Automation, Selenium, Python and Pytest

We are seeking a QA Automation Engineer (SDET) to design, develop, and maintain high-quality automated testing solutions across our UI, API, and mobile platforms. The ideal candidate is passionate about quality, experienced in Python-based automation frameworks, and comfortable working in a fast-paced, collaborative environment.

🔧 Tools & Technologies

- UI Automation: Python, Selenium- Pytest, Unittest

- API Testing: Postman/Newman, Python requests

- Mobile Automation: Appium

- Performance Testing: JMeter, Locust

- Version Control: GitHub

- CI/CD: GitHub Actions, CircleCI

📌 Key Responsibilities

- Build, enhance, and maintain automated test suites for UI, API, and mobile applications using the existing automation framework.

- Improve framework stability, performance, reporting, parallel execution, and overall maintainability.

- Execute automated test suites in CI/CD pipelines and debug, triage, and document failures with clear, actionable defect reports.

- Collaborate closely with QA, Engineering, and Product teams to understand requirements, define test strategies, and ensure robust coverage.

- Develop reusable automation components including page objects, utilities, and

Company Description

Foresiet is an AI-enabled SaaS-based Cybersecurity company that provides a comprehensive solution for Digital Risk Prevention. Leveraging the Cyber Digital Investigator platform, Foresiet proactively detects, monitors, and secures identity, data, and asset threats. Our unique combination of Human Intelligence (HUMINT) and Applied Research (HUMAN+TECHNOLOGY) protects individuals, enterprises, and the Federal Government.

Role Description

The specialist will be responsible for developing and maintaining automation processes.

1. Develop and maintain sophisticated web scraping and automation scripts, troubleshooting issues,
2. Understanding of CAPTCHA-solving approaches and working with proxy services, anti-blocking strategies: IP rotation, session management, fingerprinting/stealth, polite crawling (rate limiting, backoff), distributed workers & queueing.
3. Social Media Scraping
4. Integrate scalable captcha handling (human + automated fallbacks) and robust retry logic.
5. Make the solution deployable (Docker + orchestration) and include monitoring, logging, and clear operational documentation.
6. Creating and executing test cases, and ensuring quality assurance.
7. Collaborate with the team to identify automation opportunities and implement solutions.
8. Troubleshoot and debug technical issues related to scripts and data extraction

This is an incredible opportunity to be part of a company that has been at the forefront of AI and high-performance data storage innovation for over two decades. DataDirect Networks (DDN) is a global market leader renowned for powering many of the world's most demanding AI data centers, in industries ranging from life sciences and healthcare to financial services, autonomous cars, Government, academia, research and manufacturing.

"DDN's A3I solutions are transforming the landscape of AI infrastructure." – IDC

“The real differentiator is DDN. I never hesitate to recommend DDN. DDN is the de facto name for AI Storage in high performance environments” - Marc Hamilton, VP, Solutions Architecture & Engineering | NVIDIA

DDN is the global leader in AI and multi-cloud data management at scale. Our cutting-edge data intelligence platform is designed to accelerate AI workloads, enabling organizations to extract maximum value from their data. With a proven track record of performance, reliability, and scalability, DDN empowers businesses to tackle the most challenging AI and data-intensive workloads with confidence.

Our success is driven by our unwavering commitment to innovation, customer-centricity, and a team of passionate professionals who bring their expertise and dedication to every project. This is a chance to make a significant impact at a company that is shaping the future of AI

Greetings from LTIMindtree!!

About the job

Are you looking for a new career challenge? With LTIMindtree, are you ready to embark on a data-driven career? Working for global leading manufacturing client for providing an engaging product experience through best-in-class PIM implementation and building rich, relevant, and trusted product information across channels and digital touchpoints so their end customers can make an informed purchase decision will surely be a fulfilling experience.

If you are available for face to face at Chennai OR Bangalore kindly apply in below link.

Key Skill : Snowflake AND Sql AND Python

Interested candidates kindly apply in below link and share updated cv to hemalatha1@ltimindtree.com

Job Description

• Must have experience working as a snowflake, Data warehouse, ETL, BI projects.
• Must have experience at least two end to end implementation of Snowflake cloud data warehouse migration projects and end to end data warehouse implementations on-premise & cloud.
• Expertise in Snowflake data modelling, ELT using Snowflake SQL, implementing complex stored Procedures and standard DWH and ETL concepts.
• Expertise in Snowflake advanced concepts like setting up resource monitors, RBAC controls, virtual warehouse sizing, query performance tuning, Zero copy clone, time travel and understand how to use the

Experience: 5 years+

Responsibilities

Design and implement automation frameworks using Python and Robot Framework.

Develop and maintain automated test scripts, ensuring comprehensive test coverage.

Collaborate with teams to identify, address defects and improve test process.

Analyze test results and provide feedback to development teams.

Maximum leverage of automation for the testing needs.

Stay up to date with the latest advancements in test automation tools and techniques.

Experience: 5 years+

Responsibilities

Design and implement automation frameworks using Python and Robot Framework.

Develop and maintain automated test scripts, ensuring comprehensive test coverage.

Collaborate with teams to identify, address defects and improve test process.

Analyze test results and provide feedback to development teams.

Maximum leverage of automation for the testing needs.

Stay up to date with the latest advancements in test automation tools and techniques.

Responsibilities and Impact:

• Identify, source, and conduct comprehensive reviews of datasets from multiple sources to support climate, environmental, nature/biodiversity, and regulatory datasets
• Conduct comprehensive quality control reviews and validation of models, ensuring methodological rigor and accuracy in analytical outputs for research publications and client solutions
• Conduct data validation and model testing to support implementation of quantitative models. These models will include climate physical risk, climate transition risk and opportunity assessment, environmental impact/emissions analysis and environmental impact valuation
• Design and implement scalable data processing pipelines using Databricks
• Execute data science analytical workflows for processing large-scale datasets using established statistical modeling and machine learning frameworks
• Implement automation of analytical pipelines for climate, environmental, nature, and regulatory datasets following established protocols
• Support presentation of research findings and recommendations to stakeholders through clear visualizations and text that communicate complex data insights effectively

What We're Looking For:

Basic Required Qualifications:

• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Environmental Science, or related quantitative

Responsibilities:

- Interact with product management, project management and development teams to develop a strong understanding of the project and testing objectives

- Should co-ordinate between onshore & offshore teams

- Perform SQL queries to analyze and manipulate data.

- Participate in troubleshooting and triaging of issues with different teams to drive towards root cause identification and resolution

- Design and create test conditions, test data and test scripts to address business and technical use cases

- Use existing tools and techniques to execute test cases and build/script new tools for performing testing/validation function

Mandatory Skills Description:

- 5+ years of experience in data driven testing

- Mandatory experience in Banking or Capital Market Projects

- Good experience on PyTest and Test Automation

- Good experience in Selenium

- Strong understanding of web application testing and associated technologies.

- Proficiency in creating test plans and test cases per requirements and design

- Familiarity with continuous integration and continuous deployment (CI/CD) processes.

Nice-to-Have Skills Description:

- Knowledge of PySpark

- Understanding of Financial Markets Asset Classes (FX, FI, Equities, Rates, Commodities & Credit), various trade types (OTC, exchange traded, Spot, Forward, Swap, Options

Job title : Python Django Lead

Skills : Python, Django

Experience Required : 8-12

Location : Indore, Pune

Notice Period : Immediate to 15 days, Serving notice

Overview:

Job Overview: We are seeking an experienced Django Backend Lead Developer to join our team. The ideal candidate will have a strong background in backend development, cloud technologies, and big data processing. This role involves leading technical projects, mentoring junior developers, and ensuring the delivery of high-quality solutions.

Responsibilities:

• Lead the development of backend micro services using Django.
• Design and implement scalable and secure APIs.
• Integrate Azure Cloud services for application deployment and management.
• Utilize Azure Databricks for big data processing and analytics.
• Implement data processing pipelines using PySpark.
• Collaborate with front-end developers, product managers, and other stakeholders to deliver comprehensive solutions.
• Conduct code reviews and ensure adherence to best practices.
• Mentor and guide junior developers.
• Optimize database performance and manage data storage solutions.
• Ensure high performance and security standards for applications.
• Participate in architecture design and technical decision-making.

Qualifications:

• Bachelor's degre

Responsibilities:

- Interact with product management, project management and development teams to develop a strong understanding of the project and testing objectives

- Should co-ordinate between onshore & offshore teams

- Perform SQL queries to analyze and manipulate data.

- Participate in troubleshooting and triaging of issues with different teams to drive towards root cause identification and resolution

- Design and create test conditions, test data and test scripts to address business and technical use cases

- Use existing tools and techniques to execute test cases and build/script new tools for performing testing/validation function

Mandatory Skills Description:

- 5+ years of experience in data driven testing

- Mandatory experience in Banking or Capital Market Projects

- Good experience on PyTest and Test Automation

- Good experience in Selenium

- Strong understanding of web application testing and associated technologies.

- Proficiency in creating test plans and test cases per requirements and design

- Familiarity with continuous integration and continuous deployment (CI/CD) processes.

Nice-to-Have Skills Description:

- Knowledge of PySpark

- Understanding of Financial Markets Asset Classes (FX, FI, Equities, Rates, Commodities & Credit), various trade types (OTC, exchange traded, Spot, Forward, Swap, Options

We are looking for an experienced Python Backend Engineer to join our Simulation V&V team. The role focuses on strengthening and optimizing our metrics workflows built on ZRun (powered by Python's Ray library). You will play a key part in enabling scalable, reliable pipelines that support Sim Leap , Sim Weather , and Agent Validation initiatives.

🔧 Role Overview

As a Python Backend Engineer, you will be responsible for:

Key Responsibilities

• Investigating and resolving reliability issues in Ray‑based metrics pipelines, including:
• Memory back‑pressure
• Resource scaling limitations
• Hardening and optimizing existing workflows built on ZRun
• Building configuration tooling to enable faster, more efficient developer workflows for the SimVnV team
• Implementing CI/CD integrations to ensure pipeline consistency and prevent regressions
• (Bonus / Stretch Goal) Building basic web‑based dashboards for visualizing and interpreting metrics results

🛠 Skills & Experience Needed

• 5–7 years of experience in Python backend development
• Strong understanding of distributed computing frameworks (Ray preferred)
• Experience with metrics processing, workflow orchestration, or pipeline optimization
• Good knowledge of CI/CD s

We are looking for experienced Python Backend Developers to join our engineering team and work on scalable, high-performance backend systems.

📍 Location: Gurugram (GGN), Bengaluru (BNG), Noida

💼 Experience: 6+ years

🏢 Work model: Hybrid (3 days office per week)

🔧 Key Skills Required:

- Strong expertise in memory management, multithreading & exception handling

- Experience with FastAPI / Django frameworks

- Solid understanding of RESTful API design

- Strong SQL skills with SQLAlchemy or Django ORM

- AWS services experience (EC2, Lambda, S3, ECS, CloudWatch)

- Knowledge of design patterns (Singleton, Factory)

- Unit testing using pytest

- Data manipulation using pandas DataFrame

📩 Interested candidates, please share your updated CV to:

vijay.selvarajan@publicissapient.com

👉 Please include:

- Total Experience

- Relevant Experience

- Current CTC

- Expected CTC

- Notice Period (Immediate to 2 weeks – only apply if you can join early)

- Current Location

- Preferred Location

- LinkedIn Profile URL

#Python #BackendDeveloper #Hiring #PublicisSapient #TechJobs #SoftwareEngineering

Role: Python AI/ML Engineer

Experience: 4+ Years

Employment Type: Permanent

Work Mode: Hybrid

Office Location: Canada / USA / India (Bangalore / Chennai)

Role Overview

We are looking for a Python AI/ML Engineer with strong hands-on experience in Machine Learning, Deep Learning, and Generative AI . The candidate will be responsible for building, deploying, and scaling intelligent AI solutions in a cloud-based environment.

Key Responsibilities

• Design, develop, and deploy ML/DL models using Python
• Build and optimize Generative AI solutions leveraging LLMs such as GPT, LLaMA2, Gemini
• Implement end-to-end ML pipelines from data preprocessing to model deployment
• Work with deep learning frameworks like Keras and PyTorch
• Develop scalable solutions using microservices and containerization
• Collaborate with cross-functional teams to integrate AI models into production systems
• Deploy and manage AI/ML workloads on Azure Cloud
• Ensure model performance, reliability, and scalability in production environments.

Required Skills & Qualifications

• 4+ years of strong experience in Python programming
• Hands-on experience with ML libraries : scikit-learn, Keras,

Responsibilities and Impact:

• Identify, source, and conduct comprehensive reviews of datasets from multiple sources to support climate, environmental, nature/biodiversity, and regulatory datasets
• Conduct comprehensive quality control reviews and validation of models, ensuring methodological rigor and accuracy in analytical outputs for research publications and client solutions
• Conduct data validation and model testing to support implementation of quantitative models. These models will include climate physical risk, climate transition risk and opportunity assessment, environmental impact/emissions analysis and environmental impact valuation
• Design and implement scalable data processing pipelines using Databricks
• Execute data science analytical workflows for processing large-scale datasets using established statistical modeling and machine learning frameworks
• Implement automation of analytical pipelines for climate, environmental, nature, and regulatory datasets following established protocols
• Support presentation of research findings and recommendations to stakeholders through clear visualizations and text that communicate complex data insights effectively

What We're Looking For:

Basic Required Qualifications:

• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Environmental Science, or related quanti

Candidates ready to join immediately can share their details via email for quick processing.

📌 CCTC | ECTC | Notice Period | Location Preference

nitin.patil@ust.com

Act fast for immediate attention! ⏳📩

✅ Must‑Have Skills

• Python (Advanced), FASTAPI
• Generative AI & Agentic AI (LLMs, AI Agents, orchestration frameworks)
• AWS (ECS/EKS, Lambda, S3, DynamoDB, SageMaker)
• Distributed systems, microservices, API architecture
• DevOps/MLOps fundamentals (CI/CD, monitoring)
• System design, scalability, security, cloud architecture

✨ Good‑to‑Have Skills

• Docker, Kubernetes
• Event‑driven architecture
• MLflow, Kubeflow, Vertex AI
• Data governance, privacy, and compliance in AI systems
• LangChain, LlamaIndex, or other AI frameworks

Project Description:

As a member of our Software Engineering Group, we look first and foremost for people who are passionate about solving business problems through innovation & engineering practices. You will be required to apply your depth of knowledge and expertise to all aspects of the software development lifecycle, as well as partner continuously with your many stakeholders daily to stay focused on common goals.

Responsibilities:

As a full-stack developer, you would be responsible to drive technology delivery across cross-asset, cross-financial product implementation which are necessary for Sell and Buy side requirements of various institutional clients. You should work across other application/platform leads and business users, and trading desks to understand business vision and priorities, and help deliver cutting-edge technology by part in hands-on design and delivery of solutions. You must be highly proficient in building modern, scalable web applications and automation services, owning delivery from frontend visualization through to backend workflow implementation.

Mandatory Skills:

• CI/CD
• Full Stack
• Python
• React
• Rest API Testing

Mandatory Skills Description:

• You should have 6 to 7 years of relevant experience as a Full Stack Developer with Python and React JS, and you will be responsible for end-to-end

Job overview:

We are seeking an experienced QA Manager with 8+ years of expertise in manual testing, along with a strong background in Selenium automation and Python scripting to lead and manage quality assurance activities across multiple projects. The ideal candidate will have a proven track record in QA strategy development, test planning, team leadership, and process optimization. This role requires both technical acumen and leadership capabilities to ensure the delivery of high-quality software products in fast-paced environments. Prior experience in the healthcare domain is a must.

Responsibilities & Duties:

🔍 Leadership & Team Management:

• Lead, mentor, and manage a team of QA engineers, fostering a culture of excellence, continuous learning, and accountability.
• Define QA objectives, KPIs, and best practices to drive quality assurance processes across projects.
• Conduct performance reviews, training sessions, and workshops to enhance team skills.

📋 Test Strategy & Planning:

• Develop comprehensive QA strategies, test plans, and automation frameworks (with Selenium and Python as a plus).
• Oversee the creation of detailed test cases, test scripts, and reusable automation scenarios.
• Collaborate with stakeholders to understand business requirements and translate them into ro

Company Description

TokyoTechie is a globally recognized semiconductor architecture consulting and development company, committed to helping nations achieve technological advancement with innovative and sustainable solutions. Guided by values of integrity, teaming, and respect, TokyoTechie consistently delivers exceptional client services. The company is headquartered in multiple locations, including the USA, Germany, Dubai, and India, and serves an international client base spanning across regions like Australia, Canada, Japan, and more. With expertise in a wide range of services such as Semiconductors, blockchain development, smart contract auditing, and mobile app development, TokyoTechie is a trusted partner for leading organizations worldwide.

Role Description

This is a full-time, on-site role for a skilled professional in C++, AUTOSAR C++14, Python, and Linux, based across India (Pune, Mumbai, Bangalore, Delhi, Hyderabad, Kolkata, Kochi, Chennai). The candidate will be responsible for fix bugs as per Coverity standards , debugging, testing and fixing bugs and collaborating with cross-functional teams to solve technical challenges. The role also involves working on embedded systems and leveraging AUTOSAR C++14 standards for automotive software development. Good to have - MISRA C and debugging skills with gdb, Tools - Cursor AI agent CLI and VS Code.

Qualifications & Skills Required:

Role – Quality Engineer (Python Automation Testing)

Years of experience required – 2 to 4 Years

Location – Indore, MP – Work from office

Job Summary

We are seeking an experienced Automation Test Engineer with strong expertise in UI automation and database testing and hands-on experience in testing frameworks and CI/CD pipelines. The ideal candidate should be capable of designing, developing, and maintaining scalable automation frameworks while collaborating closely with development and DevOps teams.

The role requires strong problem-solving skills, ownership of test automation strategy, and the ability to work in complex enterprise environments involving APIs, databases, cloud platforms, and Big Data systems.

🔹 Key Responsibilities

Automation & Testing

• Design, develop, and maintain robust automation frameworks using Playwright (Preferrable) and Selenium.
• Implement UI automation for web applications with high reliability and reusability.
• Develop and execute automated test scripts using Pytest.
• Perform API automation testing using REST-based frameworks.
• Conduct RDBMS/database testing for data validation and integrity.
• Execute performance testing using JMeter.

CI/CD & DevOps Integration

• Integrate automated test suites into Jenkins-based CI/CD pipelines.


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